arXiv:2511.16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization. Iterative approaches of approximating the GP posterior using solvers like conjugate gradients, stochastic gradient descent, or alternating projections avoid cubic costs, but often require many iterations to converge, limiting their efficacy when the posterior is updated frequently with new data. To address this, we introduce three warm-start strategies that exploit solutions of small
The increasing complexity and scale of AI models necessitate more efficient computational methods, making optimizations in core algorithms like Gaussian Processes highly relevant.
Faster sequential inference in AI systems directly translates to more agile decision-making, enabling capabilities like real-time adaptation in AI agents and more efficient resource utilization.
The proposed warm-start strategies reduce convergence times for iterative Gaussian Process inference, making these methods more practical for real-world, high-frequency data updates.
- · AI developers (ML engineers)
- · Companies reliant on sequential decision-making AI
- · Robotics and autonomous systems
- · AI solutions with high latency due to inefficient inference
- · Computationally expensive, non-optimized AI approaches
AI systems using Gaussian Processes will become significantly faster and more resource-efficient.
Improved inference speed will enable more complex and responsive AI agents operating in dynamic environments.
The broader adoption of these techniques could accelerate the development and deployment of autonomous systems and advanced AI decision-making at scale.
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Read at arXiv cs.LG